AI CODING ROI

Prove what your AI coding spend shipped

Faros connects agent sessions to the pull requests, tickets, and releases they produced, and measures them against the delivery metrics your organization already tracks.

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Faros tracing AI coding spend across model providers to measured outcomes
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The problem

Adoption numbers don’t measure AI coding impact

Seat counts, token volume, and acceptance rates all tell you that
AI coding is here, but they don’t tell you what outcomes were delivered.

Outcome guesswork

Connecting AI coding activity to shipped work and business impact is imprecise and anecdotal

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Uneven returns

Two teams with the same tools and the same AI coding spend can ship vastly different outcomes

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Budgeting by instinct

Allocation, tool choice, and model standards get decided without reliable ROI evidence

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HOW FAROS HELPS

Measure outcomes, not tokens

Faros joins agent sessions to your engineering data, so AI coding outcomes are measured the way delivery has always been measured.

Connect spend to verified outcomes

Tie token spend to the outcomes you define:
merged pull requests, resolved tickets, shipped features.

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Overview of AI spend against outcomes, including cost per outcome
Outcomes frontier plotting teams by total AI spend and outcomes

See where AI coding spend is most effective

Compare cohorts on spend and shipped outcomes in a single Pareto frontier view. Find where to investigate, invest, or optimize next.

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Connect with the engineering metrics you rely on

Delivery, quality, review load, and developer experience in the same graph as token flow. Connect your agents and they can build any dashboard your team needs from it.

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Faros engineering intelligence metrics for weekly deployments and rework rate
Chart comparing how well AI coding configurations meet a task's requirements

Prove the ROI drivers before you scale

Score routes and configurations against your organization's completed work.

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LATEST UPDATES

What’s new at Faros

Research

The Speed Trap: 8 takeaways from our latest AI engineering research

AI made software development faster, but review gaps, QA bottlenecks, and rising incident volume reveal a new risk: the Speed Trap.

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AI Industry

Why cheaper AI models can cost more: The hidden model tax explained

Uncover the hidden “model tax” in cheap AI coding models. Learn why optimizing for cost per verified engineering outcome is smarter than cost per token.

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AI Industry

What is a software factory? How it works

Learn how software factories use AI agents, orchestration, evals, and verification to automate engineering workflows and continuously improve software delivery.

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See the evidence on your own codebase

Faros connects token spend to the teams, models, and merged work behind it, so you can see exactly what your
AI coding investment produced.

Stop token maxxing and start outcome maxxing.

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